Tag: ai-for-finance
All the articles with the tag "ai-for-finance".
Continuous close for FP&A
Updated:A three-part series: what a continuous (zero-day) close is and why to aim at one, one FP&A month worked old way and new, and how to build it with mainstream AI tools.
What a continuous close is, and why to aim at zero days
Zero-day close as a direction, what a continuous close actually is, who is talking about it, why the forecast is the point, and why a finance team can now build one: writing the code got cheap, owning it did not.
The use case: a contract signed on the 6th, on the forecast by the 8th
One FP&A month, old way and new: a contract that reaches the forecast in week six or on the morning of the 8th, which numbers can close early, which must wait, and the chain from purchase order to accrual that decides it.
How to build it: small processes, one log, approvals where people already are
The architecture of a continuous close: small processes orchestrated centrally, one append-only log, people at the gates, approvals in Slack or email, and a console that is only a view over the log.
Questions I asked
Questions I kept asking while designing the continuous close, with the answers I settled on: where the forecast lives, the two closes, why actuals load nightly against a monthly budget, exception queues, estimates, idempotency. A page that grows.
Continuous FP&A reporting: the build, control and evidence notes
Companion to the continuous FP&A essay: the nightly load in detail, model-against-code benchmarks, question-box rules, access, failure modes and a go-live gate.
An AI agent is a loop. Here is the loop.
The agentic loop explained for finance people: what an AI agent is, how it differs from a chat, and the ten-line algorithm underneath, with real runs.
Lotus 1-2-3 was finance's first automation. AI is the second.
The spreadsheet moved the ceiling on how much finance work one person could do, and we forgot it was ever automation. AI is moving the ceiling again. Nine things it already does inside finance teams, with pictures, a place to start, and a test for telling a good build from a dangerous one.
Citations aren't verification
What is verifiable AI? The difference between a citation and a verification, why AI arithmetic needs a calculator, and the four controls a finance team should demand.
How to use AI at work without leaking data
Is it safe to put company data in ChatGPT? A working protocol for finance teams: what to paste where, how to anonymise a question properly, and how to get IT to yes.
"Why not just upload it to ChatGPT?" The honest answer
It's the question every finance leader asks, and it deserves a straight answer, including the cases where a chatbot genuinely is the right tool.
How an AI agent searches your files (and why Excel fights back)
The four ways an AI agent can search your files (keyword, vector, file system, graph), each mapped to how you already find things, and why spreadsheets break the most popular one.